Overview

Dataset statistics

Number of variables18
Number of observations26458
Missing cells192211
Missing cells (%)40.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory144.0 B

Variable types

Numeric10
Categorical8

Alerts

2013 is highly correlated with 2014 and 6 other fieldsHigh correlation
2014 is highly correlated with 2013 and 6 other fieldsHigh correlation
2015 is highly correlated with 2013 and 6 other fieldsHigh correlation
2016 is highly correlated with 2013 and 6 other fieldsHigh correlation
2017 is highly correlated with 2013 and 6 other fieldsHigh correlation
2018 is highly correlated with 2013 and 7 other fieldsHigh correlation
2019 is highly correlated with 2013 and 7 other fieldsHigh correlation
2020 is highly correlated with 2013 and 7 other fieldsHigh correlation
LABEL2020 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2017 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2016 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2018 is highly correlated with 2018 and 9 other fieldsHigh correlation
LABEL2019 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2014 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2013 is highly correlated with LABEL2014 and 6 other fieldsHigh correlation
LABEL2015 is highly correlated with LABEL2013 and 6 other fieldsHigh correlation
LABEL2013 has 24111 (91.1%) missing values Missing
LABEL2014 has 24081 (91.0%) missing values Missing
LABEL2015 has 24080 (91.0%) missing values Missing
LABEL2016 has 24080 (91.0%) missing values Missing
LABEL2017 has 24051 (90.9%) missing values Missing
LABEL2018 has 23977 (90.6%) missing values Missing
LABEL2019 has 23938 (90.5%) missing values Missing
LABEL2020 has 23893 (90.3%) missing values Missing
2013 has 21882 (82.7%) zeros Zeros
2014 has 19464 (73.6%) zeros Zeros
2015 has 16130 (61.0%) zeros Zeros
2016 has 14553 (55.0%) zeros Zeros
2017 has 13492 (51.0%) zeros Zeros
2018 has 13328 (50.4%) zeros Zeros
2019 has 12786 (48.3%) zeros Zeros
2020 has 12053 (45.6%) zeros Zeros

Reproduction

Analysis started2022-09-22 15:21:32.640383
Analysis finished2022-09-22 15:21:48.853745
Duration16.21 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

LAT
Real number (ℝ≥0)

Distinct192
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.40020996
Minimum16.9375
Maximum17.8925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:49.102688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.9375
5-th percentile17.0325
Q117.2175
median17.3975
Q317.5775
95-th percentile17.7825
Maximum17.8925
Range0.955
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.2316771372
Coefficient of variation (CV)0.01331461734
Kurtosis-0.9851021179
Mean17.40020996
Median Absolute Deviation (MAD)0.18
Skewness0.05136507924
Sum460374.755
Variance0.05367429588
MonotonicityNot monotonic
2022-09-22T20:51:49.242547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.4775195
 
0.7%
17.4975195
 
0.7%
17.5075194
 
0.7%
17.4925194
 
0.7%
17.5025194
 
0.7%
17.3125193
 
0.7%
17.4875193
 
0.7%
17.3075193
 
0.7%
17.3175192
 
0.7%
17.4725192
 
0.7%
Other values (182)24523
92.7%
ValueCountFrequency (%)
16.93753
 
< 0.1%
16.94256
 
< 0.1%
16.947513
 
< 0.1%
16.952517
 
0.1%
16.957522
 
0.1%
16.962529
0.1%
16.967541
0.2%
16.972554
0.2%
16.977556
0.2%
16.982567
0.3%
ValueCountFrequency (%)
17.89253
 
< 0.1%
17.88759
 
< 0.1%
17.882510
 
< 0.1%
17.877511
 
< 0.1%
17.872518
0.1%
17.867528
0.1%
17.862531
0.1%
17.857536
0.1%
17.852541
0.2%
17.847542
0.2%

LON
Real number (ℝ≥0)

Distinct208
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.47750121
Minimum78.0075
Maximum79.0425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:49.391223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum78.0075
5-th percentile78.1125
Q178.2775
median78.4775
Q378.6675
95-th percentile78.8775
Maximum79.0425
Range1.035
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.2377368942
Coefficient of variation (CV)0.003029363709
Kurtosis-0.9561032173
Mean78.47750121
Median Absolute Deviation (MAD)0.195
Skewness0.1112356494
Sum2076357.727
Variance0.05651883084
MonotonicityIncreasing
2022-09-22T20:51:49.531279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.5225180
 
0.7%
78.5475179
 
0.7%
78.5175179
 
0.7%
78.5125179
 
0.7%
78.4775179
 
0.7%
78.48251178
 
0.7%
78.5425178
 
0.7%
78.4625178
 
0.7%
78.4675177
 
0.7%
78.4725177
 
0.7%
Other values (198)24674
93.3%
ValueCountFrequency (%)
78.00753
 
< 0.1%
78.01254
 
< 0.1%
78.01756
 
< 0.1%
78.02259
 
< 0.1%
78.027512
 
< 0.1%
78.032515
 
0.1%
78.0375119
0.1%
78.042521
0.1%
78.047527
0.1%
78.0525145
0.2%
ValueCountFrequency (%)
79.04251
 
< 0.1%
79.037513
 
< 0.1%
79.03255
 
< 0.1%
79.02757
< 0.1%
79.022511
< 0.1%
79.017511
< 0.1%
79.012511
< 0.1%
79.007512
< 0.1%
79.002512
< 0.1%
78.9975114
0.1%

2013
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4575
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.2147558
Minimum0
Maximum9756.0752
Zeros21882
Zeros (%)82.7%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:49.674518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile925.1541705
Maximum9756.0752
Range9756.0752
Interquartile range (IQR)0

Descriptive statistics

Standard deviation665.6123044
Coefficient of variation (CV)3.673057976
Kurtosis49.08335645
Mean181.2147558
Median Absolute Deviation (MAD)0
Skewness6.234235304
Sum4794580.01
Variance443039.7397
MonotonicityNot monotonic
2022-09-22T20:51:49.808578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021882
82.7%
558.736822
 
< 0.1%
557.903442
 
< 0.1%
1616.156981
 
< 0.1%
13.751331
 
< 0.1%
318.396331
 
< 0.1%
1347.987671
 
< 0.1%
1841.991821
 
< 0.1%
1038.430051
 
< 0.1%
1039.052251
 
< 0.1%
Other values (4565)4565
 
17.3%
ValueCountFrequency (%)
021882
82.7%
0.032191
 
< 0.1%
0.065351
 
< 0.1%
0.18661
 
< 0.1%
0.466921
 
< 0.1%
0.498371
 
< 0.1%
0.518941
 
< 0.1%
0.94221
 
< 0.1%
1.145731
 
< 0.1%
1.373431
 
< 0.1%
ValueCountFrequency (%)
9756.07521
< 0.1%
9685.373051
< 0.1%
9536.605471
< 0.1%
9107.619141
< 0.1%
9064.57521
< 0.1%
8957.394531
< 0.1%
8872.73731
< 0.1%
8367.245121
< 0.1%
8280.754881
< 0.1%
8267.025391
< 0.1%

2014
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6994
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370.6823756
Minimum0
Maximum11829.86328
Zeros19464
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:49.943276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q389.7007725
95-th percentile2184.591112
Maximum11829.86328
Range11829.86328
Interquartile range (IQR)89.7007725

Descriptive statistics

Standard deviation1106.442522
Coefficient of variation (CV)2.984880304
Kurtosis24.98761844
Mean370.6823756
Median Absolute Deviation (MAD)0
Skewness4.658906228
Sum9807514.294
Variance1224215.054
MonotonicityNot monotonic
2022-09-22T20:51:50.079670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019464
73.6%
558.736822
 
< 0.1%
10183.809571
 
< 0.1%
8808.310551
 
< 0.1%
7647.251951
 
< 0.1%
4795.027831
 
< 0.1%
6213.419431
 
< 0.1%
3387.763671
 
< 0.1%
3498.343511
 
< 0.1%
1988.775151
 
< 0.1%
Other values (6984)6984
 
26.4%
ValueCountFrequency (%)
019464
73.6%
0.032191
 
< 0.1%
0.065351
 
< 0.1%
0.466921
 
< 0.1%
0.498371
 
< 0.1%
1.145731
 
< 0.1%
1.374151
 
< 0.1%
1.543921
 
< 0.1%
1.587421
 
< 0.1%
1.668351
 
< 0.1%
ValueCountFrequency (%)
11829.863281
< 0.1%
10705.528321
< 0.1%
10586.008791
< 0.1%
10238.82911
< 0.1%
10185.724611
< 0.1%
10183.809571
< 0.1%
10181.17481
< 0.1%
10115.186521
< 0.1%
9963.309571
< 0.1%
9932.448241
< 0.1%

2015
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10327
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean611.610876
Minimum0
Maximum13333.70801
Zeros16130
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:50.211600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3555.6220725
95-th percentile3829.096146
Maximum13333.70801
Range13333.70801
Interquartile range (IQR)555.6220725

Descriptive statistics

Standard deviation1454.79882
Coefficient of variation (CV)2.378634647
Kurtosis13.49591277
Mean611.610876
Median Absolute Deviation (MAD)0
Skewness3.519816701
Sum16182000.56
Variance2116439.607
MonotonicityNot monotonic
2022-09-22T20:51:50.342467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016130
61.0%
545.431342
 
< 0.1%
553.508912
 
< 0.1%
6705.52491
 
< 0.1%
1626.400511
 
< 0.1%
89.934931
 
< 0.1%
732.258481
 
< 0.1%
571.898681
 
< 0.1%
23.738781
 
< 0.1%
25.303941
 
< 0.1%
Other values (10317)10317
39.0%
ValueCountFrequency (%)
016130
61.0%
0.259511
 
< 0.1%
0.287131
 
< 0.1%
0.441981
 
< 0.1%
0.466921
 
< 0.1%
0.480311
 
< 0.1%
0.498371
 
< 0.1%
1.145731
 
< 0.1%
1.374151
 
< 0.1%
1.453131
 
< 0.1%
ValueCountFrequency (%)
13333.708011
< 0.1%
13245.873051
< 0.1%
12632.67091
< 0.1%
11832.882811
< 0.1%
11765.667971
< 0.1%
11589.76661
< 0.1%
11110.63771
< 0.1%
10918.51661
< 0.1%
10761.903321
< 0.1%
10725.008791
< 0.1%

2016
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11905
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean908.6649698
Minimum0
Maximum15308.28418
Zeros14553
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:50.493615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3683.74295
95-th percentile5635.017672
Maximum15308.28418
Range15308.28418
Interquartile range (IQR)683.74295

Descriptive statistics

Standard deviation1899.490959
Coefficient of variation (CV)2.090419486
Kurtosis8.326804123
Mean908.6649698
Median Absolute Deviation (MAD)0
Skewness2.853740586
Sum24041457.77
Variance3608065.905
MonotonicityNot monotonic
2022-09-22T20:51:50.626141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014553
55.0%
545.431342
 
< 0.1%
1581.911251
 
< 0.1%
1801.022461
 
< 0.1%
5240.97511
 
< 0.1%
2565.994631
 
< 0.1%
1725.891361
 
< 0.1%
1286.411381
 
< 0.1%
1172.070431
 
< 0.1%
860.460141
 
< 0.1%
Other values (11895)11895
45.0%
ValueCountFrequency (%)
014553
55.0%
0.259511
 
< 0.1%
0.441981
 
< 0.1%
0.466921
 
< 0.1%
0.480311
 
< 0.1%
1.145731
 
< 0.1%
1.374151
 
< 0.1%
1.453131
 
< 0.1%
1.544921
 
< 0.1%
1.668351
 
< 0.1%
ValueCountFrequency (%)
15308.284181
< 0.1%
15274.263671
< 0.1%
13830.876951
< 0.1%
13477.228521
< 0.1%
13212.479491
< 0.1%
13006.553711
< 0.1%
12857.413091
< 0.1%
12790.96681
< 0.1%
12771.906251
< 0.1%
12601.276371
< 0.1%

2017
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12967
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1040.993616
Minimum0
Maximum15504.71875
Zeros13492
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:50.769996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3908.89009
95-th percentile6082.00422
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)908.89009

Descriptive statistics

Standard deviation2016.483117
Coefficient of variation (CV)1.937075392
Kurtosis6.674668357
Mean1040.993616
Median Absolute Deviation (MAD)0
Skewness2.589383927
Sum27542609.09
Variance4066204.16
MonotonicityNot monotonic
2022-09-22T20:51:50.899639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013492
51.0%
1185.235721
 
< 0.1%
1143.424931
 
< 0.1%
404.532681
 
< 0.1%
58.287051
 
< 0.1%
4.504381
 
< 0.1%
658.148561
 
< 0.1%
402.26261
 
< 0.1%
148.546361
 
< 0.1%
1129.05751
 
< 0.1%
Other values (12957)12957
49.0%
ValueCountFrequency (%)
013492
51.0%
0.084051
 
< 0.1%
0.259511
 
< 0.1%
0.441981
 
< 0.1%
0.586111
 
< 0.1%
1.127651
 
< 0.1%
1.351771
 
< 0.1%
1.374151
 
< 0.1%
1.453131
 
< 0.1%
1.544921
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15395.755861
< 0.1%
13878.332031
< 0.1%
13849.601561
< 0.1%
13776.42481
< 0.1%
13468.877931
< 0.1%
13252.11231
< 0.1%
13139.763671
< 0.1%
12893.333981
< 0.1%
12794.596681
< 0.1%

2018
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13131
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1058.922911
Minimum0
Maximum15504.71875
Zeros13328
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:51.037565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3935.9483525
95-th percentile6186.540235
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)935.9483525

Descriptive statistics

Standard deviation2034.872139
Coefficient of variation (CV)1.921643321
Kurtosis6.528489641
Mean1058.922911
Median Absolute Deviation (MAD)0
Skewness2.565468639
Sum28016982.37
Variance4140704.622
MonotonicityNot monotonic
2022-09-22T20:51:51.172188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013328
50.4%
2937.959961
 
< 0.1%
404.532681
 
< 0.1%
58.287051
 
< 0.1%
4.504381
 
< 0.1%
658.148561
 
< 0.1%
402.26261
 
< 0.1%
148.546361
 
< 0.1%
1129.05751
 
< 0.1%
738.94581
 
< 0.1%
Other values (13121)13121
49.6%
ValueCountFrequency (%)
013328
50.4%
0.084051
 
< 0.1%
0.259511
 
< 0.1%
0.586111
 
< 0.1%
1.127651
 
< 0.1%
1.351771
 
< 0.1%
1.374151
 
< 0.1%
1.453131
 
< 0.1%
1.544921
 
< 0.1%
1.668351
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15395.755861
< 0.1%
13878.332031
< 0.1%
13849.601561
< 0.1%
13776.42481
< 0.1%
13468.877931
< 0.1%
13252.238281
< 0.1%
13188.445311
< 0.1%
13139.763671
< 0.1%
12893.333981
< 0.1%

2019
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13673
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1089.072842
Minimum0
Maximum15504.71875
Zeros12786
Zeros (%)48.3%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:51.364364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median65.655935
Q3982.481475
95-th percentile6297.73337
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)982.481475

Descriptive statistics

Standard deviation2062.366628
Coefficient of variation (CV)1.893690254
Kurtosis6.460156517
Mean1089.072842
Median Absolute Deviation (MAD)65.655935
Skewness2.552863733
Sum28814689.27
Variance4253356.108
MonotonicityNot monotonic
2022-09-22T20:51:51.488066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012786
48.3%
634.95271
 
< 0.1%
923.920591
 
< 0.1%
660.626161
 
< 0.1%
2654.923581
 
< 0.1%
2640.261471
 
< 0.1%
1210.973141
 
< 0.1%
2598.63941
 
< 0.1%
1256.332761
 
< 0.1%
91.704981
 
< 0.1%
Other values (13663)13663
51.6%
ValueCountFrequency (%)
012786
48.3%
0.084051
 
< 0.1%
0.259511
 
< 0.1%
0.493871
 
< 0.1%
0.586111
 
< 0.1%
1.127651
 
< 0.1%
1.351771
 
< 0.1%
1.453131
 
< 0.1%
1.544921
 
< 0.1%
1.596751
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15398.26271
< 0.1%
14757.65431
< 0.1%
14688.92481
< 0.1%
13878.332031
< 0.1%
13858.428711
< 0.1%
13776.42481
< 0.1%
13468.877931
< 0.1%
13252.238281
< 0.1%
13139.763671
< 0.1%

2020
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14406
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125.372468
Minimum0
Maximum15504.71875
Zeros12053
Zeros (%)45.6%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:51.828657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median179.797
Q31031.984952
95-th percentile6382.693894
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)1031.984952

Descriptive statistics

Standard deviation2084.433683
Coefficient of variation (CV)1.852216705
Kurtosis6.243365806
Mean1125.372468
Median Absolute Deviation (MAD)179.797
Skewness2.518042048
Sum29775104.75
Variance4344863.781
MonotonicityNot monotonic
2022-09-22T20:51:51.950298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012053
45.6%
536.994631
 
< 0.1%
643.716861
 
< 0.1%
554.525211
 
< 0.1%
584.312621
 
< 0.1%
947.179141
 
< 0.1%
1818.99781
 
< 0.1%
418.283081
 
< 0.1%
58.287051
 
< 0.1%
4.504381
 
< 0.1%
Other values (14396)14396
54.4%
ValueCountFrequency (%)
012053
45.6%
0.084051
 
< 0.1%
0.493871
 
< 0.1%
0.586111
 
< 0.1%
1.127651
 
< 0.1%
1.351771
 
< 0.1%
1.453131
 
< 0.1%
1.596751
 
< 0.1%
1.668351
 
< 0.1%
2.02971
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15398.26271
< 0.1%
14757.65431
< 0.1%
14688.92481
< 0.1%
13914.39161
< 0.1%
13878.332031
< 0.1%
13858.428711
< 0.1%
13468.877931
< 0.1%
13252.02931
< 0.1%
13139.787111
< 0.1%

LABEL2013
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24111
Missing (%)91.1%
Memory size206.8 KiB
Urban
2320 
Water
 
27

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11735
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2320
 
8.8%
Water27
 
0.1%
(Missing)24111
91.1%

Length

2022-09-22T20:51:52.066358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:52.151058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2320
98.8%
water27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9388
80.0%
Uppercase Letter2347
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2347
25.0%
a2347
25.0%
b2320
24.7%
n2320
24.7%
t27
 
0.3%
e27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2320
98.8%
W27
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin11735
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

LABEL2014
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24081
Missing (%)91.0%
Memory size206.8 KiB
Urban
2350 
Water
 
27

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11885
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2350
 
8.9%
Water27
 
0.1%
(Missing)24081
91.0%

Length

2022-09-22T20:51:52.224947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:52.324839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2350
98.9%
water27
 
1.1%

Most occurring characters

ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9508
80.0%
Uppercase Letter2377
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2377
25.0%
a2377
25.0%
b2350
24.7%
n2350
24.7%
t27
 
0.3%
e27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2350
98.9%
W27
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin11885
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

LABEL2015
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24080
Missing (%)91.0%
Memory size206.8 KiB
Urban
2356 
Water
 
22

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11890
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2356
 
8.9%
Water22
 
0.1%
(Missing)24080
91.0%

Length

2022-09-22T20:51:52.400399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:52.486281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2356
99.1%
water22
 
0.9%

Most occurring characters

ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9512
80.0%
Uppercase Letter2378
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2378
25.0%
a2378
25.0%
b2356
24.8%
n2356
24.8%
t22
 
0.2%
e22
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
U2356
99.1%
W22
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin11890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

LABEL2016
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24080
Missing (%)91.0%
Memory size206.8 KiB
Urban
2360 
Water
 
18

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11890
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2360
 
8.9%
Water18
 
0.1%
(Missing)24080
91.0%

Length

2022-09-22T20:51:52.563174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:52.681558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2360
99.2%
water18
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9512
80.0%
Uppercase Letter2378
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2378
25.0%
a2378
25.0%
b2360
24.8%
n2360
24.8%
t18
 
0.2%
e18
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
U2360
99.2%
W18
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin11890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

LABEL2017
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24051
Missing (%)90.9%
Memory size206.8 KiB
Urban
2371 
Water
 
36

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12035
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2371
 
9.0%
Water36
 
0.1%
(Missing)24051
90.9%

Length

2022-09-22T20:51:52.767385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:52.865649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2371
98.5%
water36
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9628
80.0%
Uppercase Letter2407
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2407
25.0%
a2407
25.0%
b2371
24.6%
n2371
24.6%
t36
 
0.4%
e36
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U2371
98.5%
W36
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin12035
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

LABEL2018
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23977
Missing (%)90.6%
Memory size206.8 KiB
Urban
2432 
Water
 
49

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12405
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2432
 
9.2%
Water49
 
0.2%
(Missing)23977
90.6%

Length

2022-09-22T20:51:52.943188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:53.032837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2432
98.0%
water49
 
2.0%

Most occurring characters

ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9924
80.0%
Uppercase Letter2481
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2481
25.0%
a2481
25.0%
b2432
24.5%
n2432
24.5%
t49
 
0.5%
e49
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
U2432
98.0%
W49
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12405
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

LABEL2019
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23938
Missing (%)90.5%
Memory size206.8 KiB
Urban
2485 
Water
 
35

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12600
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2485
 
9.4%
Water35
 
0.1%
(Missing)23938
90.5%

Length

2022-09-22T20:51:53.122277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:53.228229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2485
98.6%
water35
 
1.4%

Most occurring characters

ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10080
80.0%
Uppercase Letter2520
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2520
25.0%
a2520
25.0%
b2485
24.7%
n2485
24.7%
t35
 
0.3%
e35
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2485
98.6%
W35
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin12600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

LABEL2020
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23893
Missing (%)90.3%
Memory size206.8 KiB
Urban
2550 
Water
 
15

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12825
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2550
 
9.6%
Water15
 
0.1%
(Missing)23893
90.3%

Length

2022-09-22T20:51:53.314982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:53.420782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2550
99.4%
water15
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10260
80.0%
Uppercase Letter2565
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2565
25.0%
a2565
25.0%
b2550
24.9%
n2550
24.9%
t15
 
0.1%
e15
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U2550
99.4%
W15
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin12825
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Interactions

2022-09-22T20:51:46.520927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-22T20:51:36.968448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:38.118392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-22T20:51:40.776978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:42.130645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-22T20:51:35.989994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-22T20:51:40.531875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-22T20:51:43.167077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-09-22T20:51:47.738711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:35.551325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:36.853025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:37.986492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:39.232654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:40.655585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:41.973666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:43.307074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:44.912314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:51:46.281084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-22T20:51:53.508267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T20:51:53.659030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T20:51:53.814177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T20:51:53.967429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-22T20:51:54.112031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T20:51:47.957740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T20:51:48.279133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T20:51:48.533928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T20:51:48.723806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

LATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
017.322578.00750.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
117.327578.00750.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
217.332578.00750.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
317.322578.01250.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
417.327578.01250.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
517.332578.01250.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
617.337578.01250.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
717.312578.01750.0636.69885636.69885636.69885636.69885636.69885636.69885636.69885NoneNoneNoneNoneNoneNoneNoneNone
817.317578.01750.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
917.322578.01750.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone

Last rows

LATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
2644817.517579.027500.00.000000.000000.000000.000000.000000.00000224.03043NoneNoneNoneNoneNoneNoneNoneNone
2644917.492579.032500.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
2645017.497579.032500.0544.25580544.25580544.43494544.43494544.43494544.43494544.43494NoneNoneNoneNoneNoneNoneNoneNone
2645117.502579.032500.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
2645217.507579.032500.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
2645317.512579.032500.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
2645417.497579.037510.0545.16174545.16174544.43805544.43805544.43805544.43805544.43805NoneNoneNoneNoneNoneNoneNoneNone
2645517.502579.037510.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
2645617.507579.037510.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone
2645717.507579.042500.00.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneNone